Publications

Awards

2023 RSNA Alexander Margulis Award

2023 Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study

2024 Taipei Biotech Awards

PanCAD.ai

2024 National Biotechnology and Medical Care Quality Award

PANCREASaver : Innovative Artificial Intelligence - assisted Detection Tool for Pancreatic Cancer

2022 Certificate of the Symbol of National Quality (SNQ)

PANCERESaver : Innovative Artificial Intelligence-assisted Detection Tool for Pancreatic Cancer

2022 National Innovation Award

Artificial Intelligence Assistance Tool for Detection of Pancreatic Cancer - PANCREASaver

2021 National Innovation Award

Artificial Intelligence Assistance Tool for Detection of Pancreatic Cancer - PANCREASaver

2020 Digestive Disease Week (DDW)

2020 “Radiomic Features Distinguish Pancreatic Cancer From Non-cancerous Pancreas,” Digestive Disease Week (DDW 2020)

Journals

The pioneer in the world

Validation of a fully automated analysis process for normal and pancreatic cancer using a nationwide dataset.

The PANCREASaver research team published the world's first fully validated deep learning model distinguishing normal and pancreatic cancer tissues in the prestigious journal 'Radiology' (2022).

Link・RSNA news

Innovative multi-dimensional radiomics hybrid analysis for pancreatic cancer.

Published in the prominent journal 'BMC Cancer,' our team presented an innovative 2D and 3D radiomics analysis method for pancreatic cancer, demonstrating its effectiveness through extensive testing on a nationwide real-world dataset (2023).

Link

Efficiency confirmation of 2D radiomics for pancreatic cancer.

Published in the esteemed journal 'Radiology: Imaging Cancer,' the team's robust research provides evidence that 2D image omics can effectively differentiate between normal and pancreatic cancer in CT scans (2021).

Link

Accurate differentiation model for normal and pancreatic cancer using deep learning.

PANCREASaver Team published a groundbreaking deep learning model that accurately distinguishes normal and pancreatic cancer tissues in the world-renowned journal The Lancet Digital Health. (2020)

Link

Dawei Chang, Po-Ting Chen, Pochuan Wang, Tinghui Wu, Andre Yanchen Yeh, Po-Chang Lee, Yi-Hui Sung, Kao-Lang Liu, Ming-Shiang Wu, Dong Yang, Holger Roth, Wei-Chih Liao*, Weichung Wang*.

Detection of Pancreatic Cancer With Two- and Three-dimensional Radiomic Analysis in a Nationwide Population-based Real-world Dataset.

BMC Cancer, 23:58, 2023.

https://doi.org/10.1186/s12885-023-10536-8

Po-Ting Chen, Tinghui Wu, Pochuan Wang, Dawei Chang, Kao-Lang Liu, Ming-Shiang Wu, Holger Roth, Po-Chang Lee, Wei-Chih Liao*, Weichung Wang*.

Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study.

Radiology, 306(1):172–182, 2023.

https://doi.org/10.1148/radiol.220152

Po-Ting Chen, Dawei Chang, Huihsuan Yen, Kao-Lang Liu, Su-Yun Huang, Holger Roth, Ming-Shiang Wu, Wei-Chih Liao*, Weichung Wang*.

Radiomic Features at CT Can Distinguish Pancreatic Cancer from Noncancerous Pancreas.

Radiology: Imaging Cancer, 3(4), 2021.

https://doi.org/10.1148/rycan.2021210010

Kao-Lang Liu, Tinghui Wu, Po-Ting Chen, Yuhsiang M. Tsai, Holger Roth, Ming-Shiang Wu, Wei-Chih Liao*, Weichung Wang*.

Deep Learning to Distinguish Pancreatic Cancer Tissue From Non-cancerous Pancreatic Tissue: a Retrospective Study With Cross-racial External Validation.

The Lancet Digital Health, Vol. 2, Iss. 6, pp. 303-313, 2020.

https://doi.org/10.1016/s2589-7500(20)30078-9

Conferences

Po-Ting Chen, Wei-Chih Liao, Dawei Chang, Pochuan Wang, Kao-Lang Liu, Ming-Shiang Wu, Weichung Wang. External Validation of Pancreatic Cancer Detection on CT with A Computer-aided Detection Tool Combining Radiomics and Deep Learning Models. Radiological Society of North America Annual Meeting (RSNA), 2022.
Chen Shen, Pochuan Wang, Dong Yang, Daguang Xu, Masahiro Oda, Po-Ting Chen, Kao-Lang Liu, Wei-Chih Liao, Chiou-Shann Fuh, Kensaku Mori, Weichung Wang* and Holger R. Roth. Joint Multi Organ and Tumor Segmentation from Partial Labels Using Federated Learning. International Workshop on Distributed, Collaborative, and Federated Learning, 2022. International Workshop on Distributed, Collaborative, and Federated Learning, 2022.

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